Untangling biological factors influencing trajectory inference from single cell dataa

Abstract: Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity towards the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unravelling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajetory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory.

Mohammed Charrout, Marcel Reinders, and Ahmed Mahfouz. Untangling biological factors influencing trajectory inference from single cell dataa. BioRxiv, 2020.
@article { bib:2020_interpret_traject_preprint,
author = { Mohammed Charrout and Marcel Reinders and Ahmed Mahfouz },
title = { Untangling biological factors influencing trajectory inference from single cell dataa },
year = { 2020 },
}
untangling single cell trajectories screenshot

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